Proactive auto-scaling

ABSTRACT

In an approach for proactive service group based auto-scaling, a processor collects usage data generated in one or more services in a container platform. A processor predicts access situation and resource utilization of the one or more services based on the usage data. A processor constructs a dynamic correlation topology among the one or more services based on the access situation and resource utilization. A processor identifies associated services correlated with the one or more services based on the dynamic correlation topology. A processor, in response to a service request exceeding a pre-set threshold, expands the one or more services and associated services.

BACKGROUND

The present disclosure relates generally to the field of cloud services,and more particularly to proactive service group based auto-scaling.

With the growing use of virtual resources, customers may encountersituations in which the virtual resources cannot accommodate their needsduring certain situations, such as unanticipated traffic spikes or needfor immediate responses to satisfy increased loads. With the number ofcustomers of a computing platform increasing, the demands on resourcesprovided by the computing platform are also increasing. In someexamples, customers may run their applications on multiple instancesacross the computing platform with certain resources allocated to eachinstance. Each instance, for example, may include a virtual serverrunning on a particular host machine of the computing platform, and mayoffer different compute and memory capabilities and resources.

Containers may be small units of application code, libraries, binaries,and dependencies. Containerization may make code portable sincecontainers may not be directly linked to the underlying infrastructure.Thus, developers can write and execute code anywhere—on a desktop, ITenvironment, or in the cloud. Containers can run on a virtualizedoperating system. A container platform may be software tools that enablethe efficient management of multiple containers running within a singleoperating system. A container platform may automate, govern, andorchestrate containers. A container platform may provide virtualized,isolated environments to run applications and their dependenciessecurely. A container platform may manage containers and the underlyinginfrastructure that runs the containers. A container platform may managethe entire container lifecycle including automation, scheduling,deployment, load balancing, scaling, and networking of containers.Managed container services may be cloud-based offerings that simplifythe building, managing, and scaling of containerized applications byrunning containers on server instances, or running containers in amanaged service.

SUMMARY

Certain shortcomings of the prior art are overcome, and additionaladvantages are provided through the provision of an approach forproactive service group based auto-scaling. Advantageously, a processorcollects usage data generated in one or more services in a containerplatform. A processor predicts access situation and resource utilizationof the one or more services based on the usage data. A processorconstructs a dynamic correlation topology among the one or more servicesbased on the access situation and resource utilization. A processoridentifies associated services correlated with the one or more servicesbased on the dynamic correlation topology. A processor, in response to aservice request exceeding a pre-set threshold, expands the one or moreservices and associated services.

In one or more embodiments, a computer-implemented method is provided tocollect usage data generated in services in a container platform.Advantageously, the usage data, for example, can be pod-level resourcestatus of pods and service access status of services. The usage data mayinclude service log data. Advantageously, the computer-implementedmethod may detect pod-level resource status of pods and service accessstatus of services through an application programming interface (API) inthe container platform. The computer-implemented method may collectservice log data as data input for subsequent modules. Advantageously,the proactive auto-scaling module may calculate service level resourceutilization based on a corresponding relationship between the servicesand the pods.

In one or more embodiments, a computer-implemented method is provided topredict access situation and resource utilization of services based onthe usage data collected from the services. Advantageously, thecomputer-implemented method may predict service request and resource.Advantageously, the computer-implemented method may construct along-term prediction model based on historical data by statisticalanalysis of the historical data from the services and the pods. Thecomputer-implemented method may integrate the historical data andshort-term real-time data to predict a future change of services.Advantageously, the computer-implemented method may take the usage dataas an input to predict the access situation and resource utilization ofeach service. In an example, considering that the service request andresource prediction may have a certain periodicity, advantageously, thecomputer-implemented method may use a combination of historicallong-term prediction model and short-term fitting. For example, theexpected value (mean) based on the statistics of the first N windows maybe used as a predicted value of the next window.

In one or more embodiments, a computer-implemented method is provided toconstruct a dynamic correlation topology among services based on theaccess situation and resource utilization of services. Advantageously,the computer-implemented method may build an initial network graph basedon prior knowledge of experts. The computer-implemented method mayperform statistics analysis on a node change of the initial networkgraph. Advantageously, the computer-implemented method may obtain thedynamic correlation topology based on similarity correlation of nodes inthe initial network. The computer-implemented method may calculate aEuclidean distance between each service statistical index.Advantageously, the computer-implemented method may associate callsbetween services by normalizing the Euclidean distance. Thecomputer-implemented method may achieve an expansion ratio calculationof the associated services through similarity. Advantageously, thecomputer-implemented method may use the correlation of nodes formodeling.

In one or more embodiments, a computer-implemented method is provided toidentify associated services correlated with services based on thedynamic correlation topology generated for services. Advantageously, inresponse to a service request exceeding a pre-set threshold, thecomputer-implemented method may expand services and associated services.Advantageously, the computer-implemented method may perform a simulationaccording to the dynamic correlation topology to view a group of thecorresponding associated services by an expert. The computer-implementedmethod may estimate an expansion size of the associated servicesaccording to a current node expansion. In an example, thecomputer-implemented method may use a service scaling controller tohandle the service group scaling. When performing the capacityexpansion, advantageously, the computer-implemented method may use theservice association probability graph obtained by the serviceassociation dynamic detector and may use the breadth of the graph totraverse and select the service with high relevance as the service groupfor capacity expansion. For example, advantageously, thecomputer-implemented method may combine expert experience and algorithmsto categorize the service group. After the establishment of theassociation graph is completed, the expert can perform a simulationaccording to the association graph to view the associated service groupand can set the threshold based on the judgment. Advantageously, thecomputer-implemented method may carry out a dynamic expansion in advancethrough a service prediction. While considering an expert experience,the computer-implemented method may consider a dynamic change of acluster to expand services. While considering the expansion of a currentnode, the computer-implemented method may expand the associated servicestogether to improve the effectiveness of expansion.

In another aspect, a computer program product is provided which includesone or more computer readable storage media, and program instructionscollectively stored on the one or more computer readable storage media.Advantageously, program instructions collect usage data generated in oneor more services in a container platform. Program instructions predictaccess situation and resource utilization of the one or more servicesbased on the usage data. Program instructions construct a dynamiccorrelation topology among the one or more services based on the accesssituation and resource utilization. Program instructions identifyassociated services correlated with the one or more services based onthe dynamic correlation topology. Program instructions, in response to aservice request exceeding a pre-set threshold, expand the one or moreservices and associated services.

In a further aspect, a computer system is provided which includes one ormore computer processors, one or more computer readable storage media,and program instructions stored on the one or more computer readablestorage media for execution by at least one of the one or more computerprocessors. Advantageously, program instructions collect usage datagenerated in one or more services in a container platform. Programinstructions predict access situation and resource utilization of theone or more services based on the usage data. Program instructionsconstruct a dynamic correlation topology among the one or more servicesbased on the access situation and resource utilization. Programinstructions identify associated services correlated with the one ormore services based on the dynamic correlation topology. Programinstructions, in response to a service request exceeding a pre-setthreshold, expand the one or more services and associated services.

Additional features and advantages are realized through the techniquesof the present invention. Other embodiments and aspects of the inventionare described in detail herein and are considered a part of the claimedinvention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram illustrating an auto-scalingenvironment, in accordance with an embodiment of the present disclosure.

FIG. 2 is a flowchart depicting operational steps of a proactiveauto-scaling module within a computing device of FIG. 1 , in accordancewith an embodiment of the present disclosure.

FIG. 3 illustrates an exemplary functional diagram of the proactiveauto-scaling module within the computing device of FIG. 1 , inaccordance with an embodiment of the present disclosure.

FIG. 4 illustrates an exemplary functional diagram of a collector of theproactive auto-scaling module within the computing device of FIG. 1 , inaccordance with an embodiment of the present disclosure.

FIG. 5 illustrates an exemplary functional diagram of a predictor of theproactive auto-scaling module within the computing device of FIG. 1 , inaccordance with an embodiment of the present disclosure.

FIG. 6 illustrates an exemplary functional diagram of a detector of theproactive auto-scaling module within the computing device of FIG. 1 , inaccordance with an embodiment of the present disclosure.

FIG. 7 illustrates an exemplary functional diagram of a controller ofthe proactive auto-scaling module within the computing device of FIG. 1, in accordance with an embodiment of the present disclosure.

FIG. 8 is a block diagram of components of the computing device of FIG.1 , in accordance with an embodiment of the present disclosure.

FIG. 9 depicts an embodiment of a cloud computing environment inaccordance with the present disclosure.

FIG. 10 depicts an embodiment of abstraction model layers of a cloudcomputing environment, in accordance with the present disclosure.

DETAILED DESCRIPTION

The present disclosure is directed to systems and methods for proactiveservice group based auto-scaling.

Embodiments of the present disclosure recognize a need for businessdevelopers to focus more on business innovation and reduce usage costs.Cloud services may provide scalability in the cloud and can effectivelyexpand according to the access situation. Embodiments of the presentdisclosure recognize a need for considering the relevance of eachcomponent in the cloud services for scalability. Embodiments of thepresent disclosure recognize a need for performing service improvements(e.g., pod expansion) in advance to cope with a future surge incommunication traffic and providing a user with a better service effect.The upstream and downstream services of some services may be delayed dueto asynchronous communication.

Embodiments of the present disclosure disclose using an applicationprogramming interface (API) provided by a container platform (e.g.,OpenShift® container platform (OCP)) and log information of each serviceto collect pod-level resource status and service access status. Thecorresponding relationship between service and pod may be used to obtainservice-level resource utilization data. Embodiments of the presentdisclosure disclose taking the data obtained by a resource collectionand organizer as input to predict the access situation and resourceutilization of each service and predicting whether there will be anabnormal increase in the service in the future. Embodiments of thepresent disclosure disclose building a network graph based on the priorknowledge of experts, performing statistics on node changes based onwindows, using the correlation of nodes for modeling, and obtainingsimilarity correlation graphs of nodes. Embodiments of the presentdisclosure disclose pre-setting a service threshold. When a request of acertain service is found to exceed the threshold, the expansion may betriggered. Embodiments of the present disclosure disclose using thecorrelation graph obtained by a service correlation topology detector tofind corresponding associated services. Embodiments of the presentdisclosure disclose concurrently calling an OCP interface to expand theservice and other related services at the same time and completing anadvance expansion operation of the service group.

Embodiments of the present disclosure disclose collecting logs andresource usage information generated in a service and providing a databasis for subsequent statistical analysis. Embodiments of the presentdisclosure disclose an API provided by an OCP to obtain pod metrics(e.g., CPU and memory) regularly. Embodiments of the present disclosuredisclose counting pod request time based on the pod's logs and obtainingan average service-level resource utilization based on correspondencebetween the service and the pod. Embodiments of the present disclosuredisclose using combination of a historical long-term prediction modeland a short-term fitting model. First, the expected value (mean) basedon the statistics of the first N windows may be used as a predictedvalue of the next window. Embodiments of the present disclosure disclosesetting a forecast period according to a specific time period. Forexample, when a traffic tends to increase sharply, the system may set anhourly forecast. On the daily working day, the system may forecast oncea day. The system may provide manual configuration, which can modify thepredicted frequency manually. At the same time, the system may usestatistical analysis of historical data to obtain information ondifferent periods of time and holidays in history to construct along-term prediction model. Due to the sudden and infrequent nature ofthe expansion, the system may use short-term data before and after theexpansion (e.g., a 3-days data before and after a special busy holiday)for model construction. Embodiments of the present disclosure discloseintegrating historical data and short-term real-time data to predict thefuture changes of the service.

Embodiments of the present disclosure disclose using a servicecorrelation topology detector to find a dynamic correlation topology.Embodiments of the present disclosure disclose constructing an initialcorrelation diagram of the service based on the architecture provided bya business personnel. The topological structure given by the businesspersonnel may tend to be biased toward the system architecture. Lessinformation may be given for the dynamic correlation call relationshipbetween services. Embodiments of the present disclosure discloseselecting short-term data before and after the time point when thehistorical expansion event occurs, such as the data within three daysfor statistical analysis. Embodiments of the present disclosure disclosecalculating the Euclidean distance between each service statisticalindex and associating the calls between services by normalizing theEuclidean distance. Embodiments of the present disclosure disclose usinga service scaling controller to handle the service group scaling.According to the CPU and memory resource conditions of the servicepredicted by the service request predictor, the service scalingcontroller may compare with the preset threshold to determine whetherthe threshold is reached. The expansion may be triggered when thethreshold is reached.

Embodiments of the present disclosure disclose carrying out dynamicexpansion in advance through a service prediction. Embodiments of thepresent disclosure disclose joining the dynamic changes of the clusterto expand the service while considering expert experience. Embodimentsof the present disclosure disclose expanding the associated servicestogether to improve the effectiveness of expansion while considering theexpansion of the current node. Embodiments of the present disclosuredisclose using an API provided by an OCP and log information of eachservice to collect pod-level resource status and service access status.Embodiments of the present disclosure disclose using the correspondingrelationship between a service and a pod to get the average servicelevel resource utilization. Embodiments of the present disclosuredisclose collecting service log data as data input for subsequentmodules. Embodiments of the present disclosure disclose taking dataobtained by a resource collection and organizer as input to predict theaccess situation and resource utilization of each service and predictingwhether there will be an abnormal increase in the service in the future.Embodiments of the present disclosure disclose building a network graphbased on the prior knowledge of experts, performing statistics on nodechanges based on windows, using the correlation of nodes for modeling,and obtaining similarity correlation graphs of nodes. Embodiments of thepresent disclosure disclose using statistical methods to analyze thesystem topology based on the results of the second step and giving aresult of the latest topology. Embodiments of the present disclosuredisclose having a service scaling controller that will pre-set theservice threshold. Embodiments of the present disclosure disclose, whenthe request of a certain service is found to exceed the threshold, theexpansion can be triggered. Embodiments of the present disclosuredisclose using the correlation graph obtained by the service correlationtopology detector to find its corresponding associated services.Embodiments of the present disclosure disclose concurrently calling anOCP interface to expand the service and associated services at the sametime and completing the advance expansion operation of the servicegroup.

Embodiments of the present disclosure disclose achieving robustness ofthe entire system by grouping all services according to the systemtopology and pre-expanding related services in a unified manner in theform of service groups, rather than the increase of a single serviceinstance. Embodiments of the present disclosure disclose using a networktopology and real-time monitoring data to automatically group eachservice in the system. Embodiments of the present disclosure disclosepredicting the future service traffic and performing expansionprocessing in advance. Embodiments of the present disclosure discloseusing the dynamic analysis of the topology to group the services of thesystem, as well as the traffic prediction through the analysis ofhistorical data and real-time monitoring data, so as to perform thepre-expansion of multiple instance groups of multiple services.Embodiments of the present disclosure disclose discovering theassociation between business service instances in the user's businesssystem and automatically performing service grouping and instancepre-expansion to ensure the robustness of the business system.

The present disclosure will now be described in detail with reference tothe Figures. FIG. 1 is a functional block diagram illustrating anauto-scaling environment, generally designated 100, in accordance withan embodiment of the present disclosure.

In the depicted embodiment, auto-scaling environment 100 includescomputing device 102, container platform 104, and network 108. Invarious embodiments of the present disclosure, container platform 104can be a platform as a service which can include cloud computingservices that allow customers to provision, instantiate, run, and managea modular bundle comprising a computing platform and one or moreapplications. Container platform 104 may allow developers to create,develop, and package such software bundles without the complexity ofbuilding and maintaining the infrastructure typically associated withdeveloping and launching the application(s). In an example, containerplatform 104 can be an OpenShift® container platform (OCP). In otherexamples, container platform 104 can be any other suitable computingplatform. In the depicted embodiment, container platform 104 includesapplication programming interface (API) 106 and service(s) 120.Service(s) 120 may include pod(s) 122. In an example, API 106 may be aset of functions, procedures, methods or classes used by computerprograms to request services from the operating system, softwarelibraries or any other service providers running on the computer. API106 may be a connection between computers or between computer programs.In an example, service(s) 120 can serve as an internal load balancer.Service(s) 120 may identify a set of replicated pods 122 in order toproxy the connections service(s) 120 receives to pods 122. Backing podscan be added to or removed from a service arbitrarily while service(s)120 remain consistently available, enabling anything that depends onservice(s) 120 to refer to service(s) 120 at a consistent address. In anexample, pod(s) 122 can be one or more containers deployed together onone host. Pod(s) 122 can be the smallest compute unit that can bedefined, deployed, and managed. For example, pods 122 may be a roughequivalent of a machine instance (physical or virtual) to a container.Each pod may be allocated its own internal IP address, therefore owningits entire port space, and containers within pods 122 can share theirlocal storage and networking. Pods 122 may have a lifecycle. Pods 122may be defined and assigned to run on a node. Pods 122 may run untiltheir container(s) exit or pods 122 may be removed for some otherreason. Pods 122, depending on policy and exit code, may be removedafter exiting, or may be retained in order to enable access to the logsof their containers.

In various embodiments of the present disclosure, computing device 102can be a laptop computer, a tablet computer, a netbook computer, apersonal computer (PC), a desktop computer, a mobile phone, asmartphone, a smart watch, a wearable computing device, a personaldigital assistant (PDA), or a server. In another embodiment, computingdevice 102 represents a computing system utilizing clustered computersand components to act as a single pool of seamless resources. In otherembodiments, computing device 102 may represent a server computingsystem utilizing multiple computers as a server system, such as in acloud computing environment. In general, computing device 102 can be anycomputing device or a combination of devices with access to proactiveauto-scaling module 110 and network 108 and is capable of processingprogram instructions and executing proactive auto-scaling module 110, inaccordance with an embodiment of the present disclosure. Computingdevice 102 may include internal and external hardware components, asdepicted and described in further detail with respect to FIG. 8 .

Further, in the depicted embodiment, computing device 102 includesproactive auto-scaling module 110. In the depicted embodiment, proactiveauto-scaling module 110 is located on computing device 102. In someembodiments, proactive auto-scaling module 110 may be located oncontainer platform 104. In some embodiments, proactive auto-scalingmodule 110 may be located externally and accessed through acommunication network such as network 108. The communication network canbe, for example, a local area network (LAN), a wide area network (WAN)such as the Internet, or a combination of the two, and may includewired, wireless, fiber optic or any other connection known in the art.In general, the communication network can be any combination ofconnections and protocols that will support communications betweencomputing device 102 and proactive auto-scaling module 110, inaccordance with a desired embodiment of the disclosure.

In one or more embodiments, proactive auto-scaling module 110 isconfigured to collect usage data generated in services 120 in containerplatform 104. The usage data, for example, can be pod-level resourcestatus of pods 122 and service access status of services 120. The usagedata may include service log data. Proactive auto-scaling module 110 maydetect pod-level resource status of pods 122 and service access statusof services 120 through API 106 in container platform 104. Proactiveauto-scaling module 110 may collect service log data as data input forsubsequent modules. Proactive auto-scaling module 110 may calculateservice level resource utilization based on a corresponding relationshipbetween services 120 and pods 122. Proactive auto-scaling module 110 mayuse API provided by container platform 104 (e.g., OpenShift® containerplatform) and the log information of each service 120 to collectpod-level resource status and service access status. Proactiveauto-scaling module 110 may use the corresponding relationship betweenservices 120 and pods 122 to obtain the average service-level resourceutilization. Proactive auto-scaling module 110 may collect logs andresource usage information generated in services 120. Proactiveauto-scaling module 110 may provide a data basis from the logs andresource usage information for subsequent statistical analysis steps.Proactive auto-scaling module 110 may use API 106 provided by containerplatform 104 to obtain pod metrics (e.g., CPU and memory) regularly.Proactive auto-scaling module 110 may count pod request time based onthe pod's logs. Proactive auto-scaling module 110 may obtain the averageservice-level resource utilization based on the correspondence betweenservices 120 and pods 122.

In one or more embodiments, proactive auto-scaling module 110 isconfigured to predict access situation and resource utilization ofservices 120 based on the usage data collected from services 120.Proactive auto-scaling module 110 may predict service request andresource. Proactive auto-scaling module 110 may construct a long-termprediction model based on historical data by statistical analysis of thehistorical data from services 120 and pods 122. Proactive auto-scalingmodule 110 may integrate the historical data and short-term real-timedata to predict a future change of services 120. Proactive auto-scalingmodule 110 may take the usage data as an input to predict the accesssituation and resource utilization of each service 120. Proactiveauto-scaling module 110 may predict whether there will be an abnormalusage increase in services 120 in the future. In an example, consideringthat the service request and resource prediction may have a certainperiodicity, proactive auto-scaling module 110 may use a combination ofhistorical long-term prediction model and short-term fitting. Forexample, first, the expected value (mean) based on the statistics of thefirst N windows may be used as a predicted value of the next window.Proactive auto-scaling module 110 may set a forecast period according toa specific period. For example, in a busy holiday season, when theonline traffic tends to increase sharply, proactive auto-scaling module110 may set an hourly forecast. On a regular day, proactive auto-scalingmodule 110 may forecast once a day. Proactive auto-scaling module 110may also provide a manual configuration option, which can modify thepredicted frequency manually. At the same time, proactive auto-scalingmodule 110 may use statistical analysis of historical data to obtaininformation on different periods of time and holidays in history toconstruct a long-term prediction model. However, due to the sudden andinfrequent nature of the expansion, proactive auto-scaling module 110may use short-term data before and after the expansion (such as threedays data before and after a special holiday, e.g., when users tend touse internet services sharply) for model construction. Proactiveauto-scaling module 110 may integrate historical data and short-termreal-time data to predict the future changes of services 120.

In one or more embodiments, proactive auto-scaling module 110 isconfigured to construct a dynamic correlation topology among services120 based on the access situation and resource utilization of services120. Proactive auto-scaling module 110 may build an initial networkgraph based on prior knowledge of experts. Proactive auto-scaling module110 may perform statistics analysis on a node change of the initialnetwork graph. Proactive auto-scaling module 110 may obtain the dynamiccorrelation topology based on similarity correlation of nodes in theinitial network. Proactive auto-scaling module 110 may select short-termdata before and after a time point when a historical expansion eventoccurs. Proactive auto-scaling module 110 may calculate a Euclideandistance between each service statistical index. Proactive auto-scalingmodule 110 may associate calls between services 120 by normalizing theEuclidean distance. Proactive auto-scaling module 110 may achieve anexpansion ratio calculation of the associated services throughsimilarity. Proactive auto-scaling module 110 may build a network graphbased on the prior knowledge of experts. Proactive auto-scaling module110 may perform statistics on node changes based on windows. Proactiveauto-scaling module 110 may use the correlation of nodes for modeling.Proactive auto-scaling module 110 may obtain similarity correlationgraphs of nodes. Proactive auto-scaling module 110 may use a servicecorrelation topology detector to find the dynamic correlation topology.Proactive auto-scaling module 110 may construct the initial correlationdiagram of services 120 based on an architecture provided by, forexample, business personnel. In some cases, the topological structuregiven by the business personnel may tend to be biased toward the systemarchitecture. Less information may be given for the dynamic correlationcall relationship between services 120. At the same time, considering asudden or infrequent situation during expansion, proactive auto-scalingmodule 110 may select the short-term data before and after the timepoint when the historical expansion event occurs, for example, the datawithin three days for statistical analysis. Proactive auto-scalingmodule 110 may calculate the Euclidean distance between each servicestatistical index. Proactive auto-scaling module 110 may associate thecalls between services by normalizing the Euclidean distance. Throughthe similarity to achieve the expansion ratio calculation of theassociated service, proactive auto-scaling module 110 may provideguidance for the expansion of the next module.

In one or more embodiments, proactive auto-scaling module 110 isconfigured to identify associated services correlated with services 120based on the dynamic correlation topology generated for service 120. Inresponse to a service request exceeding a pre-set threshold, proactiveauto-scaling module 110 may expand services 120 and associated servicesat the same time. Proactive auto-scaling module 110 may use the dynamiccorrelation topology to find the corresponding associated services withservices 120. Proactive auto-scaling module 110 may perform a simulationaccording to the dynamic correlation topology to view a group of thecorresponding associated services by an expert. Proactive auto-scalingmodule 110 may set a threshold based on judgment from the expert.Proactive auto-scaling module 110 may estimate an expansion size of theassociated services according to a current node expansion. Proactiveauto-scaling module 110 may compare the service request to the presetthreshold to determine whether the preset threshold is reached.Proactive auto-scaling module 110 may trigger the expansion when thethreshold is reached. Proactive auto-scaling module 110 may determineservices 120 with high relevance as a service group for capacityexpansion based on the dynamic correlation topology and a breadth of thedynamic correlation topology. Proactive auto-scaling module 110 mayconcurrently call API 106 to expand services and associated services 120at the same time to complete the advance expansion operation of theservice group. In an example, proactive auto-scaling module 110 maypre-set the service threshold. When the request of a certain service isfound to exceed the threshold, proactive auto-scaling module 110 maytrigger the expansion. Proactive auto-scaling module 110 may use thecorrelation graph obtained by the service correlation topology detectorto find its corresponding associated services. Proactive auto-scalingmodule 110 may concurrently call an OCP interface (e.g., API 106) toexpand the service and related services at the same time and to completethe advance expansion operation of the service group.

In an example, proactive auto-scaling module 110 may use a servicescaling controller to handle the service group scaling. For example,based on CPU and memory resource conditions of the service predicted byproactive auto-scaling module 110, proactive auto-scaling module 110 maycompare the service request with the preset threshold to determinewhether the threshold is reached. Proactive auto-scaling module 110 maytrigger the expansion when the threshold is reached. When performing thecapacity expansion, proactive auto-scaling module 110 may use theservice association probability graph obtained by the serviceassociation dynamic detector and may use the breadth of the graph totraverse and select the service with high relevance as the service groupfor capacity expansion. For example, proactive auto-scaling module 110may combine expert experience and algorithms to categorize the servicegroup. After the establishment of the association graph is completed,the expert can perform a simulation according to the association graphto view the associated service group and can set the threshold based onthe judgment. For example, when the similarity reaches a certainsimilarity value, e.g., 0.8, proactive auto-scaling module 110 may putthe associated service into a same group. When the association group isdetermined, proactive auto-scaling module 110 may estimate the expansionsize of the associated service according to the current node expansion.

In an example, proactive auto-scaling module 110 may carry out a dynamicexpansion in advance through a service prediction. While considering anexpert experience, proactive auto-scaling module 110 may consider adynamic change of a cluster to expand services 120. While consideringthe expansion of a current node, proactive auto-scaling module 110 mayexpand the associated services together to improve the effectiveness ofexpansion. Proactive auto-scaling module 110 may adjust the scale of thecluster based on the active situation. Proactive auto-scaling module 110may group all services according to the system topology andpre-expanding related services in a unified manner in the form ofservice groups. Proactive auto-scaling module 110 may utilize networktopology and real-time monitoring data to automatically group eachservice in the system. Proactive auto-scaling module 110 may predict thefuture service traffic and may perform expansion processing in advance.Proactive auto-scaling module 110 may expand a series of related servicegroups in advance. Proactive auto-scaling module 110 may perform thedynamic analysis of the topology to group the services of the system, aswell as the traffic prediction through the analysis of historical dataand real-time monitoring data. Proactive auto-scaling module 110 mayperform a pre-expansion of multiple instance groups of multipleservices. Proactive auto-scaling module 110 may discover the associationbetween business service instances in the user's business system and mayautomatically perform service grouping and instance pre-expansion toensure the robustness of the business system. Proactive auto-scalingmodule 110 may use real-time monitoring data to discover the associatedtopology between various business service instances in the user'sbusiness system and may automatically perform service grouping andinstance pre-expansion based on the network topology to ensure therobustness of the business system. Proactive auto-scaling module 110 mayautomatically group the business systems according to the networktopology and may perform unified expansion based on the service group.

In the depicted embodiment, proactive auto-scaling module 110 includescollector 112, predictor 114, detector 116 and controller 118. In thedepicted embodiment, collector 112, predictor 114, detector 116 andcontroller 118 are located on computing device 102 and proactiveauto-scaling module 110. In some embodiments, collector 112, predictor114, detector 116 and controller 118 may be located on containerplatform 104. In some embodiments, collector 112, predictor 114,detector 116 and controller 118 may be located externally and accessedthrough a communication network such as network 108.

In one or more embodiments, collector 112 is configured to collect usagedata generated in services 120 in container platform 104. The usagedata, for example, can be pod-level resource status of pods 122 andservice access status of services 120. The usage data may includeservice log data. Collector 112 may detect pod-level resource status ofpods 122 and service access status of services 120 through API 106 incontainer platform 104. Collector 112 may collect service log data asdata input for subsequent modules. Collector 112 may calculate servicelevel resource utilization based on a corresponding relationship betweenservices 120 and pods 122. Collector 112 may use API provided bycontainer platform 104 (e.g., OpenShift® container platform) and the loginformation of each service 120 to collect pod-level resource status andservice access status. Collector 112 may use the correspondingrelationship between services 120 and pods 122 to obtain the averageservice-level resource utilization. Collector 112 may collect logs andresource usage information generated in services 120. Collector 112 mayprovide a data basis from the logs and resource usage information forsubsequent statistical analysis steps. Collector 112 may use API 106provided by container platform 104 to obtain pod metrics (e.g., CPU andmemory) regularly. Collector 112 may count pod request time based on thepod's logs. Collector 112 may obtain the average service-level resourceutilization based on the correspondence between services 120 and pods122.

In one or more embodiments, predictor 114 is configured to predictaccess situation and resource utilization of services 120 based on theusage data collected from services 120. Predictor 114 may predictservice request and resource. Predictor 114 may construct a long-termprediction model based on historical data by statistical analysis of thehistorical data from services 120 and pods 122. Predictor 114 mayintegrate the historical data and short-term real-time data to predict afuture change of services 120. Predictor 114 may take the usage data asan input to predict the access situation and resource utilization ofeach service 120. Predictor 114 may predict whether there will be anabnormal usage increase in services 120 in the future. In an example,considering that the service request and resource prediction may have acertain periodicity, predictor 114 may use a combination of historicallong-term prediction model and short-term fitting. For example, first,the expected value (mean) based on the statistics of the first N windowsmay be used as a predicted value of the next window. Predictor 114 mayset a forecast period according to a specific period. For example, in abusy holiday season, when the online traffic tends to increase sharply,predictor 114 may set an hourly forecast. On a regular day, predictor114 may forecast once a day. Predictor 114 may also provide a manualconfiguration option, which can modify the predicted frequency manually.At the same time, predictor 114 may use statistical analysis ofhistorical data to obtain information on different periods of time andholidays in history to construct a long-term prediction model. However,due to the sudden and infrequent nature of the expansion, predictor 114may use short-term data before and after the expansion (such as threedays data before and after a special holiday, e.g., when users tend touse internet services sharply) for model construction. Predictor 114 mayintegrate historical data and short-term real-time data to predict thefuture changes of services 120.

In one or more embodiments, detector 116 is configured to construct adynamic correlation topology among services 120 based on the accesssituation and resource utilization of services 120. Detector 116 maybuild an initial network graph based on prior knowledge of experts.Detector 116 may perform statistics analysis on a node change of theinitial network graph. Detector 116 may obtain the dynamic correlationtopology based on similarity correlation of nodes in the initialnetwork. Detector 116 may select short-term data before and after a timepoint when a historical expansion event occurs. Detector 116 maycalculate an Euclidean distance between each service statistical index.Detector 116 may associate calls between services 120 by normalizing theEuclidean distance. Detector 116 may achieve an expansion ratiocalculation of the associated services through similarity. Detector 116may build a network graph based on the prior knowledge of experts.Detector 116 may perform statistics on node changes based on windows.Detector 116 may use the correlation of nodes for modeling. Detector 116may obtain similarity correlation graphs of nodes. Detector 116 may usea service correlation topology detector to find the dynamic correlationtopology. Detector 116 may construct the initial correlation diagram ofservices 120 based on an architecture provided by, for example, businesspersonnel. In some cases, the topological structure given by thebusiness personnel may tend to be biased toward the system architecture.Less information may be given for the dynamic correlation callrelationship between services 120. At the same time, considering asudden or infrequent situation during expansion, detector 116 may selectthe short-term data before and after the time point when the historicalexpansion event occurs, for example, the data within three days forstatistical analysis. Detector 116 may calculate the Euclidean distancebetween each service statistical index. Detector 116 may associate thecalls between services by normalizing the Euclidean distance. Throughthe similarity to achieve the expansion ratio calculation of theassociated service, detector 116 may provide guidance for the expansionof the next module.

In one or more embodiments, controller 118 is configured to identifyassociated services correlated with services 120 based on the dynamiccorrelation topology generated for service 120. In response to a servicerequest exceeding a pre-set threshold, controller 118 may expandservices 120 and associated services at the same time. Controller 118may use the dynamic correlation topology to find the correspondingassociated services with services 120. Controller 118 may perform asimulation according to the dynamic correlation topology to view a groupof the corresponding associated services by an expert. Controller 118may set a threshold based on judgment from the expert. Controller 118may estimate an expansion size of the associated services according to acurrent node expansion. Controller 118 may compare the service requestto the preset threshold to determine whether the preset threshold isreached. Controller 118 may trigger the expansion when the threshold isreached. Controller 118 may determine services 120 with high relevanceas a service group for capacity expansion based on the dynamiccorrelation topology and a breadth of the dynamic correlation topology.Controller 118 may concurrently call API 106 to expand services andassociated services 120 at the same time to complete the advanceexpansion operation of the service group. In an example, controller 118may pre-set the service threshold. When the request of a certain serviceis found to exceed the threshold, controller 118 may trigger theexpansion. Controller 118 may use the correlation graph obtained by theservice correlation topology detector to find its correspondingassociated services. Controller 118 may concurrently call an OCPinterface (e.g., API 106) to expand the service and related services atthe same time and to complete the advance expansion operation of theservice group.

In an example, controller 118 may use a service scaling controller tohandle the service group scaling. For example, based on CPU and memoryresource conditions of the service predicted by controller 118,controller 118 may compare the service request with the preset thresholdto determine whether the threshold is reached. Controller 118 maytrigger the expansion when the threshold is reached. When performing thecapacity expansion, controller 118 may use the service associationprobability graph obtained by the service association dynamic detectorand may use the breadth of the graph to traverse and select the servicewith high relevance as the service group for capacity expansion. Forexample, controller 118 may combine expert experience and algorithms tocategorize the service group. After the establishment of the associationgraph is completed, the expert can perform a simulation according to theassociation graph to view the associated service group and can set thethreshold based on the judgment. For example, when the similarityreaches a certain similarity value, e.g., 0.8, controller 118 may putthe associated service into a same group. When the association group isdetermined, controller 118 may estimate the expansion size of theassociated service according to the current node expansion.

In an example, controller 118 may carry out a dynamic expansion inadvance through a service prediction. While considering an expertexperience, controller 118 may consider a dynamic change of a cluster toexpand services 120. While considering the expansion of a current node,controller 118 may expand the associated services together to improvethe effectiveness of expansion. Controller 118 may adjust the scale ofthe cluster based on the active situation. Controller 118 may group allservices according to the system topology and pre-expanding relatedservices in a unified manner in the form of service groups. Controller118 may utilize network topology and real-time monitoring data toautomatically group each service in the system. Controller 118 maypredict the future service traffic and may perform expansion processingin advance. Controller 118 may expand a series of related service groupsin advance. Controller 118 may perform the dynamic analysis of thetopology to group the services of the system, as well as the trafficprediction through the analysis of historical data and real-timemonitoring data. Controller 118 may perform a pre-expansion of multipleinstance groups of multiple services. Controller 118 may discover theassociation between business service instances in the user's businesssystem and may automatically perform service grouping and instancepre-expansion to ensure the robustness of the business system.Controller 118 may use real-time monitoring data to discover theassociated topology between various business service instances in theuser's business system and may automatically perform service groupingand instance pre-expansion based on the network topology to ensure therobustness of the business system. Controller 118 may automaticallygroup the business systems according to the network topology and mayperform unified expansion based on the service group.

FIG. 2 is a flowchart 200 depicting operational steps of proactiveauto-scaling module 110 in accordance with an embodiment of the presentdisclosure.

Proactive auto-scaling module 110 operates to collect usage datagenerated in services 120 in container platform 104. Proactiveauto-scaling module 110 operates to predict access situation andresource utilization of services 120 based on the usage data. Proactiveauto-scaling module 110 operates to construct a dynamic correlationtopology among services 120 based on the access situation and resourceutilization. Proactive auto-scaling module 110 operates to identifyassociated services correlated with services 120 based on the dynamiccorrelation topology. Proactive auto-scaling module 110 operates to, inresponse to a service request exceeding a pre-set threshold, expandservices 120 and associated services.

In step 202, proactive auto-scaling module 110 collects usage datagenerated in services 120 in container platform 104. The usage data, forexample, can be pod-level resource status of pods 122 and service accessstatus of services 120. The usage data may include service log data.Proactive auto-scaling module 110 may detect pod-level resource statusof pods 122 and service access status of services 120 through API 106 incontainer platform 104. Proactive auto-scaling module 110 may collectservice log data as data input for subsequent modules. Proactiveauto-scaling module 110 may calculate service level resource utilizationbased on a corresponding relationship between services 120 and pods 122.Proactive auto-scaling module 110 may use API provided by containerplatform 104 (e.g., OpenShift® container platform) and the loginformation of each service 120 to collect pod-level resource status andservice access status. Proactive auto-scaling module 110 may use thecorresponding relationship between services 120 and pods 122 to obtainthe average service-level resource utilization. Proactive auto-scalingmodule 110 may collect logs and resource usage information generated inservices 120. Proactive auto-scaling module 110 may provide a data basisfrom the logs and resource usage information for subsequent statisticalanalysis steps. Proactive auto-scaling module 110 may use API 106provided by container platform 104 to obtain pod metrics (e.g., CPU andmemory) regularly. Proactive auto-scaling module 110 may count podrequest time based on the pod's logs. Proactive auto-scaling module 110may obtain the average service-level resource utilization based on thecorrespondence between services 120 and pods 122.

In step 204, proactive auto-scaling module 110 predicts access situationand resource utilization of services 120 based on the usage datacollected from services 120. Proactive auto-scaling module 110 maypredict service request and resource. Proactive auto-scaling module 110may construct a long-term prediction model based on historical data bystatistical analysis of the historical data from services 120 and pods122. Proactive auto-scaling module 110 may integrate the historical dataand short-term real-time data to predict a future change of services120. Proactive auto-scaling module 110 may take the usage data as aninput to predict the access situation and resource utilization of eachservice 120. Proactive auto-scaling module 110 may predict whether therewill be an abnormal usage increase in services 120 in the future. In anexample, considering that the service request and resource predictionmay have a certain periodicity, proactive auto-scaling module 110 mayuse a combination of historical long-term prediction model andshort-term fitting. For example, first, the expected value (mean) basedon the statistics of the first N windows may be used as a predictedvalue of the next window. Proactive auto-scaling module 110 may set aforecast period according to a specific period. For example, in a busyholiday season, when the online traffic tends to increase sharply,proactive auto-scaling module 110 may set an hourly forecast. On aregular day, proactive auto-scaling module 110 may forecast once a day.Proactive auto-scaling module 110 may also provide a manualconfiguration option, which can modify the predicted frequency manually.At the same time, proactive auto-scaling module 110 may use statisticalanalysis of historical data to obtain information on different periodsof time and holidays in history to construct a long-term predictionmodel. However, due to the sudden and infrequent nature of theexpansion, proactive auto-scaling module 110 may use short-term databefore and after the expansion (such as three days data before and aftera special holiday, e.g., when users tend to use internet servicessharply) for model construction. Proactive auto-scaling module 110 mayintegrate historical data and short-term real-time data to predict thefuture changes of services 120.

In step 206, proactive auto-scaling module 110 constructs a dynamiccorrelation topology among services 120 based on the access situationand resource utilization of services 120. Proactive auto-scaling module110 may build an initial network graph based on prior knowledge ofexperts. Proactive auto-scaling module 110 may perform statisticsanalysis on a node change of the initial network graph. Proactiveauto-scaling module 110 may obtain the dynamic correlation topologybased on similarity correlation of nodes in the initial network.Proactive auto-scaling module 110 may select short-term data before andafter a time point when a historical expansion event occurs. Proactiveauto-scaling module 110 may calculate an Euclidean distance between eachservice statistical index. Proactive auto-scaling module 110 mayassociate calls between services 120 by normalizing the Euclideandistance. Proactive auto-scaling module 110 may achieve an expansionratio calculation of the associated services through similarity.Proactive auto-scaling module 110 may build a network graph based on theprior knowledge of experts. Proactive auto-scaling module 110 mayperform statistics on node changes based on windows. Proactiveauto-scaling module 110 may use the correlation of nodes for modeling.Proactive auto-scaling module 110 may obtain similarity correlationgraphs of nodes. Proactive auto-scaling module 110 may use a servicecorrelation topology detector to find the dynamic correlation topology.Proactive auto-scaling module 110 may construct the initial correlationdiagram of services 120 based on an architecture provided by, forexample, business personnel. In some cases, the topological structuregiven by the business personnel may tend to be biased toward the systemarchitecture. Less information may be given for the dynamic correlationcall relationship between services 120. At the same time, considering asudden or infrequent situation during expansion, proactive auto-scalingmodule 110 may select the short-term data before and after the timepoint when the historical expansion event occurs, for example, the datawithin three days for statistical analysis. Proactive auto-scalingmodule 110 may calculate the Euclidean distance between each servicestatistical index. Proactive auto-scaling module 110 may associate thecalls between services by normalizing the Euclidean distance. Throughthe similarity to achieve the expansion ratio calculation of theassociated service, proactive auto-scaling module 110 may provideguidance for the expansion of the next module.

In step 208, proactive auto-scaling module 110 identifies associatedservices correlated with services 120 based on the dynamic correlationtopology generated for service 120. Proactive auto-scaling module 110may use the dynamic correlation topology to find the correspondingassociated services with services 120. Proactive auto-scaling module 110may perform a simulation according to the dynamic correlation topologyto view a group of the corresponding associated services by an expert.Proactive auto-scaling module 110 may set a threshold based on judgmentfrom the expert. Proactive auto-scaling module 110 may estimate anexpansion size of the associated services according to a current nodeexpansion. Proactive auto-scaling module 110 may determine services 120with high relevance as a service group for capacity expansion based onthe dynamic correlation topology and a breadth of the dynamiccorrelation topology. Proactive auto-scaling module 110 may use thecorrelation graph obtained by the service correlation topology detectorto find its corresponding associated services. In an example, proactiveauto-scaling module 110 may use a service scaling controller to handlethe service group scaling. When performing the capacity expansion,proactive auto-scaling module 110 may use the service associationprobability graph obtained by the service association dynamic detectorand may use the breadth of the graph to traverse and select the servicewith high relevance as the service group for capacity expansion. Forexample, proactive auto-scaling module 110 may combine expert experienceand algorithms to categorize the service group. After the establishmentof the association graph is completed, the expert can perform asimulation according to the association graph to view the associatedservice group and can set the threshold based on the judgment. Forexample, when the similarity reaches a certain similarity value, e.g.,0.8, proactive auto-scaling module 110 may put the associated serviceinto a same group. When the association group is determined, proactiveauto-scaling module 110 may estimate the expansion size of theassociated service according to the current node expansion.

In step 210, proactive auto-scaling module 110 expands services 120 andassociated services at the same time when a service request exceeds apre-set threshold. Proactive auto-scaling module 110 may set a thresholdbased on judgment from the expert. Proactive auto-scaling module 110 mayestimate an expansion size of the associated services according to acurrent node expansion. Proactive auto-scaling module 110 may comparethe service request to the preset threshold to determine whether thepreset threshold is reached. Proactive auto-scaling module 110 maytrigger the expansion when the threshold is reached. Proactiveauto-scaling module 110 may determine services 120 with high relevanceas a service group for capacity expansion based on the dynamiccorrelation topology and a breadth of the dynamic correlation topology.Proactive auto-scaling module 110 may concurrently call API 106 toexpand services and associated services 120 at the same time to completethe advance expansion operation of the service group. In an example,proactive auto-scaling module 110 may pre-set the service threshold.When the request of a certain service is found to exceed the threshold,proactive auto-scaling module 110 may trigger the expansion. In anexample, proactive auto-scaling module 110 may use a service scalingcontroller to handle the service group scaling. For example, based onCPU and memory resource conditions of the service predicted by proactiveauto-scaling module 110, proactive auto-scaling module 110 may comparethe service request with the preset threshold to determine whether thethreshold is reached. Proactive auto-scaling module 110 may trigger theexpansion when the threshold is reached. When performing the capacityexpansion, proactive auto-scaling module 110 may use the serviceassociation probability graph obtained by the service associationdynamic detector and may use the breadth of the graph to traverse andselect the service with high relevance as the service group for capacityexpansion. For example, proactive auto-scaling module 110 may combineexpert experience and algorithms to categorize the service group. Afterthe establishment of the association graph is completed, the expert canperform a simulation according to the association graph to view theassociated service group and can set the threshold based on thejudgment. For example, when the similarity reaches a certain similarityvalue, e.g., 0.8, proactive auto-scaling module 110 may put theassociated service into a same group. When the association group isdetermined, proactive auto-scaling module 110 may estimate the expansionsize of the associated service according to the current node expansion.

In an example, proactive auto-scaling module 110 may carry out a dynamicexpansion in advance through a service prediction. While considering anexpert experience, proactive auto-scaling module 110 may consider adynamic change of a cluster to expand services 120. While consideringthe expansion of a current node, proactive auto-scaling module 110 mayexpand the associated services together to improve the effectiveness ofexpansion. Proactive auto-scaling module 110 may adjust the scale of thecluster based on the active situation. Proactive auto-scaling module 110may group all services according to the system topology andpre-expanding related services in a unified manner in the form ofservice groups. Proactive auto-scaling module 110 may utilize networktopology and real-time monitoring data to automatically group eachservice in the system. Proactive auto-scaling module 110 may predict thefuture service traffic and may perform expansion processing in advance.Proactive auto-scaling module 110 may expand a series of related servicegroups in advance. Proactive auto-scaling module 110 may perform thedynamic analysis of the topology to group the services of the system, aswell as the traffic prediction through the analysis of historical dataand real-time monitoring data. Proactive auto-scaling module 110 mayperform a pre-expansion of multiple instance groups of multipleservices. Proactive auto-scaling module 110 may discover the associationbetween business service instances in the user's business system and mayautomatically perform service grouping and instance pre-expansion toensure the robustness of the business system. Proactive auto-scalingmodule 110 may use real-time monitoring data to discover the associatedtopology between various business service instances in the user'sbusiness system and may automatically perform service grouping andinstance pre-expansion based on the network topology to ensure therobustness of the business system. Proactive auto-scaling module 110 mayautomatically group the business systems according to the networktopology and may perform unified expansion based on the service group.

FIG. 3 illustrates an exemplary functional diagram of proactiveauto-scaling module 110, in accordance with an embodiment of the presentdisclosure.

In the example of FIG. 3 , proactive auto-scaling module 110 includescollector 112 (e.g., resource collection and organizer), predictor 114(e.g., service request predictor), detector 116 (e.g., servicecorrelation topology detector) and controller 118 (e.g., service scalingcontroller). Collector 112 may collect usage data generated in services120 (e.g., “service1”, “service2”, “service3”, “middleware1”,“middleware2”) in container platform 104 (e.g., OCP). The usage data,for example, can be pod-level resource status of pods 122 and serviceaccess status of services 120. Collector 112 may detect pod-levelresource status of pods 122 and service access status of services 120through API 106 in container platform 104. Collector 112 may collectservice log data as data input for subsequent modules (e.g., predictor114 and detector 116). Predictor 114 may predict access situation andresource utilization of services 120 based on the usage data collectedfrom services 120. Predictor 114 may construct a long-term predictionmodel based on historical data by statistical analysis of the historicaldata from services 120 and pods 122. Predictor 114 may integrate thehistorical data and short-term real-time data to predict a future changeof services 120. Detector 116 may construct a dynamic correlationtopology among services 120 based on the access situation and resourceutilization of services 120. Controller 118 may identify associatedservices correlated with services 120 based on the dynamic correlationtopology generated for service 120. In response to a service requestexceeding a pre-set threshold, controller 118 may expand services 120and associated services at the same time.

FIG. 4 illustrates an exemplary functional diagram of collector 112 ofproactive auto-scaling module 110, in accordance with an embodiment ofthe present disclosure.

In the example of FIG. 4 , collector 112 may collect usage datagenerated in services 120 in container platform 104. The usage data, forexample, can be pod-level resource 402 status of pods 122 and serviceaccess status of services 120. The usage data may include service log404 data. Collector 112 may calculate service level resource utilization408 based on a corresponding relationship 406 between services 120 andpods 122. Collector 112 may use API 106 provided by container platform104 to obtain pod metrics (e.g., CPU and memory) regularly. Collector112 may count pod request time based on the pod's logs. Collector 112may obtain the average service-level resource utilization based on thecorrespondence between services 120 and pods 122.

FIG. 5 illustrates an exemplary functional diagram of predictor 114 ofproactive auto-scaling module 110, in accordance with an embodiment ofthe present disclosure.

In the example of FIG. 5 , predictor 114 may predict access situationand resource utilization of services 120 based on the usage datacollected from services 120. Predictor 114 may predict service requestand resource. Predictor 114 may construct a long-term prediction model502 based on historical data 504 by statistical analysis of historicaldata 504 from services 120 and pods 122. Predictor 114 may integratehistorical data 504 and short-term real-time data 506 to predict futurechanges 508 of services 120. In an example, considering that the servicerequest and resource prediction may have a certain periodicity,predictor 114 may use a combination of historical long-term predictionmodel and short-term fitting. For example, first, the expected value(mean) based on the statistics of first N windows 510 may be used as apredicted value of the next window. Predictor 114 may set forecastperiod 512 according to a specific period. For example, in a busyholiday season, when the online traffic tends to increase sharply,predictor 114 may set an hourly forecast. On a regular day, predictor114 may forecast once a day. Predictor 114 may also provide a manualconfiguration option, which can modify the predicted frequency manually.At the same time, predictor 114 may use statistical analysis 514 ofhistorical data to obtain information on different periods of time andholidays in history to construct long-term prediction model 502.However, due to the sudden and infrequent nature of the expansion,predictor 114 may use short-term data 506 before and after the expansion(such as three days data before and after a special holiday, e.g., whenusers tend to use internet services sharply) for model construction.Predictor 114 may integrate historical data 504 and short-term real-timedata 506 to predict future changes 508 of services 120.

FIG. 6 illustrates an exemplary functional diagram of detector 116 ofproactive auto-scaling module 110, in accordance with an embodiment ofthe present disclosure.

In the example of FIG. 6 , detector 116 may construct dynamiccorrelation topology 604 among services 120 based on the accesssituation and resource utilization of services 120. Detector 116 maybuild initial network graph 602 based on prior knowledge of experts.Detector 116 may perform statistics analysis on a node change of initialnetwork graph 602. Detector 116 may obtain dynamic correlation topology604 based on similarity correlation of nodes in initial network graph602. Detector 116 may select short-term data before and after a timepoint 606 when a historical expansion event occurs. Detector 116 maycalculate Euclidean distance 608 between each service statistical index.Detector 116 may associate calls between services 120 by normalizing 610Euclidean distance 608. Detector 116 may achieve an expansion ratiocalculation of the associated services through similarity.

FIG. 7 illustrates an exemplary functional diagram of controller 118 ofproactive auto-scaling module 110, in accordance with an embodiment ofthe present disclosure.

In the example of FIG. 7 , controller 118 may identify associatedservices correlated with services 120 based on dynamic correlationtopology 604 generated for service 120. In response to a service requestexceeding pre-set threshold 702, in block 704, controller 118 may expandservices 120 and associated services at the same time. Controller 118may use dynamic correlation topology 604 to find the correspondingassociated services with services 120. Controller 118 may perform asimulation according to dynamic correlation topology 604 to view a groupof the corresponding associated services by an expert. Controller 118may compare the service request to the preset threshold to determinewhether preset threshold 702 is reached. Controller 118 may triggerexpansion when the threshold is reached. Controller 118 may determineservices 120 with high relevance as a service group for capacityexpansion based on dynamic correlation topology 604 and breadth 706 ofthe dynamic correlation topology. Controller 118 may concurrently callAPI 106 to expand services and associated services 120 at the same timeto complete the advance expansion operation of the service group 708.Controller 118 may use dynamic correlation topology 604 to find itscorresponding associated services. Controller 118 may concurrently callan OCP interface (e.g., API 106) to expand services 120 and relatedservices at the same time and to complete the advance expansionoperation of service group 708.

FIG. 8 depicts a block diagram 800 of components of computing device 102in accordance with an illustrative embodiment of the present disclosure.It should be appreciated that FIG. 8 provides only an illustration ofone implementation and does not imply any limitations with regard to theenvironments in which different embodiments may be implemented. Manymodifications to the depicted environment may be made.

Computing device 102 may include communications fabric 802, whichprovides communications between cache 816, memory 806, persistentstorage 808, communications unit 810, and input/output (I/O)interface(s) 812. Communications fabric 802 can be implemented with anyarchitecture designed for passing data and/or control informationbetween processors (such as microprocessors, communications and networkprocessors, etc.), system memory, peripheral devices, and any otherhardware components within a system. For example, communications fabric802 can be implemented with one or more buses or a crossbar switch.

Memory 806 and persistent storage 808 are computer readable storagemedia. In this embodiment, memory 806 includes random access memory(RAM). In general, memory 806 can include any suitable volatile ornon-volatile computer readable storage media. Cache 816 is a fast memorythat enhances the performance of computer processor(s) 804 by holdingrecently accessed data, and data near accessed data, from memory 806.

Proactive auto-scaling module 110 may be stored in persistent storage808 and in memory 806 for execution by one or more of the respectivecomputer processors 804 via cache 816. In an embodiment, persistentstorage 808 includes a magnetic hard disk drive. Alternatively, or inaddition to a magnetic hard disk drive, persistent storage 808 caninclude a solid state hard drive, a semiconductor storage device,read-only memory (ROM), erasable programmable read-only memory (EPROM),flash memory, or any other computer readable storage media that iscapable of storing program instructions or digital information.

The media used by persistent storage 808 may also be removable. Forexample, a removable hard drive may be used for persistent storage 808.Other examples include optical and magnetic disks, thumb drives, andsmart cards that are inserted into a drive for transfer onto anothercomputer readable storage medium that is also part of persistent storage808.

Communications unit 810, in these examples, provides for communicationswith other data processing systems or devices. In these examples,communications unit 810 includes one or more network interface cards.Communications unit 810 may provide communications through the use ofeither or both physical and wireless communications links. Proactiveauto-scaling module 110 may be downloaded to persistent storage 808through communications unit 810.

I/O interface(s) 812 allows for input and output of data with otherdevices that may be connected to computing device 102. For example, I/Ointerface 812 may provide a connection to external devices 818 such as akeyboard, keypad, a touch screen, and/or some other suitable inputdevice. External devices 818 can also include portable computer readablestorage media such as, for example, thumb drives, portable optical ormagnetic disks, and memory cards. Software and data used to practiceembodiments of the present invention, e.g., proactive auto-scalingmodule 110 can be stored on such portable computer readable storagemedia and can be loaded onto persistent storage 808 via I/O interface(s)812. I/O interface(s) 812 also connect to display 820.

Display 820 provides a mechanism to display data to a user and may be,for example, a computer monitor.

The programs described herein are identified based upon the applicationfor which they are implemented in a specific embodiment of theinvention. However, it should be appreciated that any particular programnomenclature herein is used merely for convenience, and thus theinvention should not be limited to use solely in any specificapplication identified and/or implied by such nomenclature.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Python, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a computer, or other programmable data processing apparatusto produce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks. These computerreadable program instructions may also be stored in a computer readablestorage medium that can direct a computer, a programmable dataprocessing apparatus, and/or other devices to function in a particularmanner, such that the computer readable storage medium havinginstructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be accomplished as one step, executed concurrently,substantially concurrently, in a partially or wholly temporallyoverlapping manner, or the blocks may sometimes be executed in thereverse order, depending upon the functionality involved. It will alsobe noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The terminology used herein was chosen to best explain the principles ofthe embodiment, the practical application or technical improvement overtechnologies found in the marketplace, or to enable others of ordinaryskill in the art to understand the embodiments disclosed herein.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 9 , illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 9 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 10 , a set of functional abstraction layersprovided by cloud computing environment 50 (FIG. 9 ) is shown. It shouldbe understood in advance that the components, layers, and functionsshown in FIG. 10 are intended to be illustrative only and embodiments ofthe invention are not limited thereto. As depicted, the following layersand corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and module 96 including, for example,proactive auto-scaling module 110 as described above with respect toauto-scaling environment 100.

Although specific embodiments of the present invention have beendescribed, it will be understood by those of skill in the art that thereare other embodiments that are equivalent to the described embodiments.Accordingly, it is to be understood that the invention is not to belimited by the specific illustrated embodiments, but only by the scopeof the appended claims.

What is claimed is:
 1. A computer-implemented method comprising:collecting, by one or more processors, usage data generated in one ormore services in a container platform; predicting, by one or moreprocessors, access situation and resource utilization of the one or moreservices based on the usage data, wherein predicting access situationand resource utilization comprises: constructing a long-term predictionmodel based on historical data by statistical analysis of the historicaldata; and integrating the historical data and short-term real-time datato predict a future change of the one or more services; constructing, byone or more processors, a dynamic correlation topology among the one ormore services based on the access situation and resource utilization;identifying, by one or more processors, associated services correlatedwith the one or more services based on the dynamic correlation topology;and in response to a service request exceeding a pre-set threshold,expanding, by one or more processors, the one or more services andassociated services.
 2. The computer-implemented method of claim 1,wherein constructing the dynamic correlation topology comprises:building an initial network graph based on prior knowledge of experts;performing statistics analysis on a node change of the initial networkgraph; and obtaining the dynamic correlation topology based onsimilarity correlation of nodes in the initial network.
 3. Thecomputer-implemented method of claim 1, wherein detecting the dynamiccorrelation topology comprises: selecting short-term data before andafter a time point when a historical expansion event occurs; calculatinga Euclidean distance between each service statistical index; associatingcalls between services by normalizing the Euclidean distance; andachieving an expansion ratio calculation of the associated servicesthrough similarity.
 4. The computer-implemented method of claim 1,wherein identifying the associated services correlated with the one ormore services comprises: using the dynamic correlation topology to findthe corresponding associated services with the one or more services;performing a simulation according to the dynamic correlation topology toview a group of the corresponding associated services by an expert;setting a threshold based on judgment from the expert; and estimating anexpansion size of the associated services according to a current nodeexpansion.
 5. The computer-implemented method of claim 1, whereinexpanding the one or more services and associated services comprises:comparing the service request to the preset threshold to determine thatthe preset threshold is reached; triggering the expansion when thepreset threshold is reached; determining the services with highrelevance as a service group for capacity expansion based on the dynamiccorrelation topology and a breadth of the dynamic correlation topology;and concurrently calling an API to expand the one or more services andassociated services at the same time to complete the advance expansionoperation of the service group.
 6. A computer-implemented methodcomprising: collecting, by one or more processors, usage data generatedin one or more services in a container platform; predicting, by one ormore processors, access situation and resource utilization of the one ormore services based on the usage data; constructing, by one or moreprocessors, a dynamic correlation topology among the one or moreservices based on the access situation and resource utilization, whereinconstructing the dynamic correlation topology comprises: building aninitial network graph based on prior knowledge of experts; performingstatistics analysis on a node change of the initial network graph; andobtaining the dynamic correlation topology based on similaritycorrelation of nodes in the initial network; identifying, by one or moreprocessors, associated services correlated with the one or more servicesbased on the dynamic correlation topology; and in response to a servicerequest exceeding a pre-set threshold, expanding, by one or moreprocessors, the one or more services and associated services.
 7. Thecomputer-implemented method of claim 6, wherein predicting accesssituation and resource utilization comprises: constructing a long-termprediction model based on historical data by statistical analysis of thehistorical data; and integrating the historical data and short-termreal-time data to predict a future change of the one or more services.8. The computer-implemented method of claim 6, wherein detecting thedynamic correlation topology comprises: selecting short-term data beforeand after a time point when a historical expansion event occurs;calculating a Euclidean distance between each service statistical index;associating calls between services by normalizing the Euclideandistance; and achieving an expansion ratio calculation of the associatedservices through similarity.
 9. The computer-implemented method of claim1, wherein identifying the associated services correlated with the oneor more services comprises: using the dynamic correlation topology tofind the corresponding associated services with the one or moreservices; performing a simulation according to the dynamic correlationtopology to view a group of the corresponding associated services by anexpert; setting a threshold based on judgment from the expert; andestimating an expansion size of the associated services according to acurrent node expansion.
 10. The computer-implemented method of claim 1,wherein expanding the one or more services and associated servicescomprises: comparing the service request to the preset threshold todetermine that the preset threshold is reached; triggering the expansionwhen the preset threshold is reached; determining the services with highrelevance as a service group for capacity expansion based on the dynamiccorrelation topology and a breadth of the dynamic correlation topology;and concurrently calling an API to expand the one or more services andassociated services at the same time to complete the advance expansionoperation of the service group.
 11. A computer-implemented methodcomprising: collecting, by one or more processors, usage data generatedin one or more services in a container platform; predicting, by one ormore processors, access situation and resource utilization of the one ormore services based on the usage data; constructing, by one or moreprocessors, a dynamic correlation topology among the one or moreservices based on the access situation and resource utilization;identifying, by one or more processors, associated services correlatedwith the one or more services based on the dynamic correlation topology,wherein identifying the associated services correlated with the one ormore services comprises: using the dynamic correlation topology to findthe corresponding associated services with the one or more services;performing a simulation according to the dynamic correlation topology toview a group of the corresponding associated services by an expert;setting a threshold based on judgment from the expert; and estimating anexpansion size of the associated services according to a current nodeexpansion; and in response to a service request exceeding a pre-setthreshold, expanding, by one or more processors, the one or moreservices and associated services.
 12. The computer-implemented method ofclaim 11, wherein predicting access situation and resource utilizationcomprises: constructing a long-term prediction model based on historicaldata by statistical analysis of the historical data; and integrating thehistorical data and short-term real-time data to predict a future changeof the one or more services.
 13. The computer-implemented method ofclaim 11, wherein constructing the dynamic correlation topologycomprises: building an initial network graph based on prior knowledge ofexperts; performing statistics analysis on a node change of the initialnetwork graph; and obtaining the dynamic correlation topology based onsimilarity correlation of nodes in the initial network.
 14. Thecomputer-implemented method of claim 11, wherein detecting the dynamiccorrelation topology comprises: selecting short-term data before andafter a time point when a historical expansion event occurs; calculatinga Euclidean distance between each service statistical index; associatingcalls between services by normalizing the Euclidean distance; andachieving an expansion ratio calculation of the associated servicesthrough similarity.
 15. The computer-implemented method of claim 1,wherein expanding the one or more services and associated servicescomprises: comparing the service request to the preset threshold todetermine that the preset threshold is reached; triggering the expansionwhen the preset threshold is reached; determining the services with highrelevance as a service group for capacity expansion based on the dynamiccorrelation topology and a breadth of the dynamic correlation topology;and concurrently calling an API to expand the one or more services andassociated services at the same time to complete the advance expansionoperation of the service group.